Data quality has always been important and that continues to be fact. However, because business data requires thorough cleansing and preparation to be used as input to any analytics or business intelligence system, it might be even more important than ever. DATAVERSITY brought this interesting information to our attention in their article, “The Impact of Data Quality in the Machine Learning Era.”
In an era of automated and self-service business analytics, average business users do not often have prior knowledge or skills to differentiate between bad and good data, but they are suddenly equipped with advanced analytics tools for extracting competitive and actionable intelligence from piles of complex data. In today’s technological and competitive world, every organization needs a well-designed and sustainable data strategy to combat the obvious complexities of multi-source, multi-type, and high volumes of data seemingly coming from every technological source.
There is a machine learning algorithm that learns from available data. Thus, data must be accurate and complete to be a reliable teaching source. For technologies like big data to succeed in the future business ecosystem, artificial intelligence and machine learning tools have to adapt.
Melody K. Smith
Sponsored by Access Innovations, the world leader in thesaurus, ontology, and taxonomy creation and metadata application.
